Brigham Young University

NIH Research Projects · FY 2026 · 2026-06

Project Summary/Abstract A nervous system requires a wide diversity of cell types for an organism to properly function. This diversity must be both established during development and maintained over the life of an animal. Neuronal cell type identity is characterized by distinct patterns of gene expression, morphology, connectivity and function. The neurons derived from the two Q neuroblasts, QL and QR, in the model organism C. elegans provide a genetically and experimentally accessible model for elucidating mechanisms of generating and maintaining neuronal cell fate and connectivity. The Q neuroblasts ultimately generate three pairs of neurons through identical differentiation patterns, with QL producing three neurons on the left side of the animal (named PQR, PVM and SDQL), and QR producing three neurons on the right (named AQR, AVM, and SDQR). From single- cell RNA sequencing experiments, we have demonstrated that the left and right neurons in each of the three pairs are transcriptionally distinct, despite their similar lineages. Electron microscopy reconstructions show that although SDQL and SDQR contact similar sets of cells, they synapse onto distinct postsynaptic partners. This proposal is aimed at understanding the molecular mechanisms underlying the differences between the left and right members of each pair of Q-derived neurons (AQR/PQR, AVM/PVM and SDQR/SDQL). Previous studies and our preliminary data have shown that the Hox transcription factor MAB-5 (ANTP in Drosophila/Hox6-8 in vertebrates) is expressed in QL descendent but not QR descendent neurons. Using endogenous reporters of neuropeptide expression, we have validated transcriptional differences between SDQL and SDQR. We have shown that mab-5, the gene encoding MAB-5, is required for the both the initial differential expression of the neuropeptide nlp-64 between SDQR and SDQL and for maintaining this differential expression after development. We hypothesize that MAB-5 is the major transcription factor driving transcriptional differences between the left and right members of all three Q-derived neuron pairs. We also hypothesize that MAB-5 regulates the differences in the postsynaptic partners between SDQR and SDQL. In Aim 1, we will examine the effect of mab-5 deletion and ectopic expression in QR-derived neurons on downstream gene expression, using both endogenous reporters throughout development and single-cell RNA sequencing to assess transcriptome wide differences in mab-5 loss of function mutants. In Aim 2, we will use the auxin-inducible degron system to degrade MAB-5 after initial development and use single-cell RNA sequencing to detect transcriptome wide changes. This will test our hypothesis that MAB-5 is required continuously to maintain cell identity. In Aim 3, we will label SDQ synapses and test the hypothesis that loss of MAB-5 will cause SDQL to acquire SDQR-like connectivity and ectopic expression of MAB-5 will cause SDQR to acquire SDQL-like connectivity. Our proposed work will broaden our understanding of the generation and maintenance of cell diversity and synaptic specificity.

NSF Awards · FY 2026 · 2026-04

The 2026 IEEE North American School for Information Theory (NASIT 2026) will be held at Brigham Young University from June 21-26, 2025. This grant provides support for the expenses of school participants from the United States such as graduate students and postdoctoral fellows. Attendees will benefit by learning from senior colleagues, presenting their own research in poster sessions, and expanding their network with the research community. This award will cover accommodation and travel for students. The IEEE North American School for Information Theory is an annual event of the IEEE Information Theory Society; 2026 will be the 18th year the school will have held the event. The school provides a supportive environment where foundations for learning and long-term future scientific collaborations are established. The event delivers interactive education for graduate students in mathematics, engineering, and computer sciences. It presents students the opportunity to meet and learn from senior researchers in academia and industry who present long-format tutorials. Student and postdoctoral participants present their research results in poster sessions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

This collaborative research project aims to synergize advancements in artificial intelligence (AI) and mathematics to enhance computational methods for mathematical reasoning and expedite mathematical discovery. The project brings together a team of experts from the mathematical sciences, computer science, and AI, leveraging their complementary skills to tackle complex problems in these intersecting fields. The research will focus on developing AI models that can reason constructively about complex mathematical problems, improving formal proof systems, and creating new AI tools that integrate mathematical intuition and creativity. Additionally, the project seeks to advance AI with mathematical foundations, aiming for more interpretable, controllable, and trustworthy AI models. By addressing both the advancement of mathematical research through AI and the enhancement of AI with mathematical insights, the project aims to create significant breakthroughs in both areas, ultimately contributing to broader societal impacts and scientific knowledge. More specifically, this project investigates how to endow AI systems with the ability to reason constructively and intuitively about complex mathematical problems, using techniques from reinforcement learning, generative modeling, and formal proof verification. Central to the research is the modeling of theorem proving as a sequential decision-making process, where formal proofs are framed as trajectories through combinatorially structured state and action spaces. The team will develop scalable task embeddings to quantify the complexity and diversity of reasoning tasks, enabling curriculum learning strategies and improved training data generation. Ideas from intrinsic motivation such as novelty and surprise will guide proof-space exploration in settings where reward signals are sparse or delayed. The project also aims to construct interpretable and elegant proofs by identifying efficient trajectories through the reasoning space, aligned with human-interpretable landmarks, and to develop alignment metrics for selecting models suited to specific problem types. In parallel, the team will investigate the mathematical foundations of neural architectures, analyzing the representational power and optimization of transformer-based models in formal reasoning contexts. Generative models will be applied to construct counterexamples and structured mathematical objects, providing tools for discovery in mathematical domains such as knot theory, group theory, and algebraic geometry. Through these integrated efforts, the project seeks to advance both the development of mathematically grounded AI systems and the use of AI as a tool for mathematical research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

Inflammatory bowel disease (IBD) affects millions of adults, significantly impairing quality of life and posing substantial challenges in diagnosis, monitoring, and treatment. Current methods for assessing and treating IBD have significant limitations, including invasive procedures, lack of localized monitoring, and suboptimal drug delivery. This project aims to develop a novel tethered capsule system for precise, prolonged localization within the gastrointestinal (GI) tract, enabling site-specific monitoring and targeted therapy delivery for IBD. The long-term goal is to revolutionize IBD management through this minimally invasive platform. The project will pursue three specific aims: (1) Design and fabricate a tethered capsule system optimized for prolonged GI retention, (2) Design and evaluate a cheek anchoring and release system, and (3) Evaluate the localization and retention capabilities of the system in a synthetic model. The research will employ innovative approaches, including a novel tethered odometry mechanism for precise positioning, an auto-halting navigation system, and a biocompatible, degradable tether. The project will use advanced mechanical design, materials science, and gastroenterology to develop and test the system. Expected outcomes include a fully functional prototype capable of maintaining localization within 2 cm of target sites for 12 hours under simulated GI conditions. This technology has the potential to transform IBD management by enabling continuous, site-specific monitoring and targeted drug delivery, potentially improving treatment efficacy, reducing systemic side effects, and decreasing healthcare costs associated with IBD. This research aligns with the National Institute of Biomedical Imaging and Bioengineering's mission to develop innovative technologies improving human health. It suits the R15 Academic Research Enhancement Award (AREA) mechanism, which stimulates research in institutions without major NIH support. Conducted at Brigham Young University, a primarily undergraduate institution, the project will provide valuable research opportunities for students in biomedical engineering and related fields. Students will gain skills in medical device development, data analysis, and scientific communication through hands-on involvement. The project's multidisciplinary nature will foster cross-departmental collaborations, strengthening the university's research environment and capacity for biomedical research. Its focus on mentoring undergraduate students aligns with the R15 program's goal of preparing a workforce for national biomedical, behavioral, and clinical research needs.

NIH Research Projects · FY 2025 · 2025-09

Project Summary Alzheimer’s disease (AD) is among the most significant public health and medical challenges of our day. Approximately 7 million Americans are living with AD, and without effective interventions, the number will double in the next 25 years. Homogenous datasets limit the clinical utility of discoveries, possibly leading to race-based disparities in therapeutics and diagnostic tools. Almost all AD research data were derived from majority-White populations in high-income countries. Native Hawaiians and Pacific Islanders (NHPIs) have exceptionally high risk. Yet, despite being the second fastest-growing racial minority group, NHPIs are the least represented racial minority group in large repositories/datasets. The ADSP was initiated, in part, to solve the genetic architecture of AD. While limited data exist, available evidence suggests that the genetic architecture of AD in NHPIs is unique. For example, the APOE SNPs are not correlated with AD in Chamorros or Polynesians. We plan to do the following. Aim 1. Recruit and Collect Data from 5,000 NHPIs. We will recruit 1,000 NHPIs annually through our network of connections, including community and religious leaders, social media, radio, TV, personal connections, and word of mouth. Each participant will complete thorough health, medical, social determinants of health, diet, physical activity, and demographics surveys; a neurophysical exam; provide a blood sample; and AD testing. We will collect whole genome sequences, SNP Array data, and standard laboratory assays for each participant. Aim 2. Participant Diagnosis. Each participant will be diagnosed using the NACC battery and adjudicated following the protocols established by ACAD with minor adaptations to make the assessments culturally appropriate for NHPIs. Aim 3. Genetic Analyses. We will describe NHPI genetics (e.g., estimate SNP frequencies in NHPIs), analyze NHPI population structure, and conduct the first AD GWAS in NHPIs.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY/ABSTRACT With obesity prevalence at never-before-seen incidences, there is an imperative need to identify modifiable behavioral and physiologic targets to reduce obesogenic risk factors. Poor sleep is a modifiable therapeutic target that recent neuroimaging research indicates plays a vital role in obesity. However, the mechanisms that drive the relationship between poor sleep and obesity are elusive and unclear. New and advanced neuroimaging and sleep methods hold promise in helping to determine when and how poor sleep imparts risk for physical health disorders generally and obesity specifically. Sleep neuroimaging uses advanced functional neuroimaging techniques concurrent with polysomnographic methods to obtain a clearer and more detailed picture of brain activation patterns that occurs during sleep; such novel information can help elucidate previously undiscovered mechanisms linking sleep with obesity so that the underlying mechanisms can be more directly targeted in prevention and treatment efforts. There is increasing realization that sleep, and its restorative functions, are regionalized processes localized in the brain and can become disrupted regionally. In our model, we propose that regionalized sleep disturbance prevents restorative benefit to those regions, thereby resulting in daytime impairments specific to those brain regions affected during sleep, a process called local sleep disturbance. This comprehensive sleep neuroimaging study includes gold-standard measures at various levels of analysis (e.g., dim-light melatonin onset assessment, polysomnography, actigraphy, and self-reported sleep questionnaires and diaries) in conjunction with functional magnetic resonance neuroimaging during non-rapid eye movement sleep to understand (Aim 1) how functional connectivity during sleep relates to obesity-related outcomes (i.e., dietary behaviors, sedentary behavior, neural activation in brain regions associated with food-related reward and inhibition), (Aim 2) how differences in network-level functional connectivity during sleep, as a marker of local sleep disturbance, may mediate the association between poor sleep and obesity-related outcomes, and (Aim 3) explore how the relationship between functional connectivity during sleep and obesogenic risk factors differ across individual factors including developmental status (adolescents and young adults) and sex. This research will use innovative methodologies to uncover critical mechanisms that link poor sleep with obesity, which has the potential to inform preclinical models sleep health and general wellbeing. Furthermore, this research will provide support for meritorious research at an undergraduate-focused institution (Brigham Young University) by providing undergraduate students with advanced, active biomedical research experience, all of which will ultimately strengthen the research environment present at Brigham Young University and feed the sleep medicine pipeline with well trained and experienced individuals.

NSF Awards · FY 2025 · 2025-09

With the support of the Chemical Catalysis Program of the Division of Chemistry, Dr. Daniel Ess and Dr. David Michaelis at Brigham Young University in collaboration with the Chevron Phillips Chemical Company will use computational methods and machine learning/data science techniques to design, synthesize, and test new homogeneous retro-hydroformylation catalysts that selectively generate alpha-olefins. Developing new catalysts is critical to discovering new and selective chemical reactions that can impact the chemical industry. An important chemical reaction for homogeneous catalyst development is retro-hydroformylation that converts aldehydes to terminal 1-alkenes (called alpha-olefins) because these products are key precursors for the synthesis of many commodity chemicals, such as plastics, lubricants, and surfactants. Currently, there are no known industrially viable homogeneous retro-hydroformylation catalysts and research scientists are only using trial-and-error catalyst development tactics. This work holds significant promise for translating new catalyst designs to the chemical industry. Also, this work provides unique training for undergraduate students, graduate students, and postdoctoral scholars at the interface between computational chemistry, machine learning, and experimental training for preparation to enter the chemical industry workforce. Homogeneous catalysts being investigated are second and third row transition metal complexes with bespoke designed phosphine ligands. The project will develop and apply approaches to combine molecular computational chemistry with data science to predict catalysts that have high reactivity and selectivity. The project will test computational predictions and develop fundamental catalysis understanding through experimentally synthesizing and testing catalysts that work through both acceptor/transfer and acceptor-less conditions. These efforts support training of undergraduate students, graduate students, and postdoctoral scholars in state-of-the-art computational chemistry and machine learning techniques as well as advanced experimental reaction techniques. Students will also interface with and learn from industrial chemists and engineers at Chevron Phillips Chemical. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This award supports an experimental study of energy transport in collision-dominated ultracold plasmas. Ultracold plasma can serve as a model system for extremely dense and hot plasmas generated in laser-driven nuclear fusion experiments, which create matter that is hotter than the Sun and more dense than solid metal. Improved understanding of such plasmas would enable faster progress towards development of nuclear fusion energy sources, as well as address national nuclear security priorities. This research project will test portions of the detailed computer models used to predict transport processes in hot, dense plasmas by conducting precision laser measurements to create small-scale ultracold plasmas and measure everything about them – how the ions collide, how energy is transferred, and how the plasma approaches equilibrium. These small-scale plasmas are prepared with adjustable shapes and with different kinds of atoms, sometimes in combination with strong magnetic fields, so that transport processes in them will mimic the transport processes that occur in hot, dense plasmas. Testing the computer models and highlighting ways they can reach higher fidelity will help advance plasma science in the national interest. Radiation-hydrodynamic codes successfully capture the temperature, density, and neutron yield of high energy laser experiments that are designed to produce plasmas close to the hydrodynamic limit. However, many current and planned experiments are far from this limit. Codes to model these high energy density plasmas (HEDPs) must include kinetic effects, which are challenging to validate because many diagnostics yield integrated quantities such as effective temperatures and average or line-integrated densities. Experimental access to the underlying distribution functions in HEDPs is nearly impossible. Instead, a new method for measuring the ion distribution functions in model systems, called ultracold neutral plasmas (UNPs), has been developed at Brigham Young University. With proper energy scaling, UNPs are thermodynamically equivalent to HEDPs. The new experiments supported under this project will probe gaps in existing theoretical understanding and computer models of ion jetting, interfacial mixing, electron-ion thermalization, and ion stopping power. These are all critical "kinetic" processes that occur in technologically-relevant HEDP plasmas. Using precision laser spectroscopy and advanced analysis techniques, the project team will measure how the ion distribution functions evolve. By making precision measurements in the UNP environment and then comparing them to predictions from kinetic codes, the codes will be tested while avoiding the complications of quantum potentials, electron degeneracy, high optical opacity, and impossibly short time scales. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Modern agriculture is essential for feeding the world's growing population, but weedy plants that invade crop fields cause billions of dollars in losses annually and threaten food security. While most non-crop plants struggle to survive in agricultural environments, some species have rapidly evolved to thrive in these human-managed landscapes, becoming persistent problems for farmers. This research investigates how these weedy plants evolved so successfully, using a plant genus called Amaranthus that includes some of the most troublesome agricultural weeds in North America. Understanding the biological mechanisms that allow certain plants to quickly adapt to new environments like agricultural systems is crucial for developing more effective, sustainable weed management strategies and for predicting which species might become future problems. This knowledge will help farmers and agricultural scientists stay ahead of evolving challenges while also advancing our broader understanding of how organisms adapt to rapidly changing conditions. The project will advance education by training students spanning urban and rural communities, through a new plant evolution curriculum, creating international genomics workshops, and engaging local communities through citizen science projects that help track weed distributions while building scientific literacy. This research will generate high-quality genome-wide data for nearly all species in the genus Amaranthus, which contains 11 globally important agricultural weeds alongside non-weedy relatives, providing an unprecedented dataset for studying convergent evolution in response to agriculture. The project combines phylogenomic analyses across the genus with detailed population genomic studies of three focal weed species (A. palmeri, A. retroflexus, and A. albus) collected from agricultural and natural habitats. Common garden experiments will test hypotheses about key traits that facilitate weediness, including germination under stress conditions, competitive ability, and phenotypic plasticity. Advanced comparative genomic methods will identify regions of accelerated evolution in weedy lineages and distinguish between different sources of adaptive genetic variation, including ancestral polymorphisms, introgression between species, and parallel mutations. The researchers will integrate genomic and phenotypic data through phylogenetic genome-wide association studies to map the connections between genotype, phenotype, and environmental selection pressures. This multi-scale approach will reveal the genetic architecture underlying convergent adaptation to agricultural environments and provide insights into the repeatability of evolutionary responses to human-mediated environmental change. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This Engineering Research Initiation (ERI) project will support research that looks to advance the field of robotics by developing a new class of robots that can change their overall shape, safely interact with humans, manipulate loads many times greater than their own weight, and be robust to individual component failure. These robots consist of an interconnected network of robotic balloons that inflate and deflate to produce coordinated movement throughout the entire collective. These networked balloon robots have potential applications including healthcare, eldercare, and disaster response. In healthcare settings the soft, conforming nature of the balloons will safely and comfortably allow assistance with everyday tasks such as switching between sitting, standing and reclining postures. In disaster response, networked balloon robots could aid in search and rescue efforts by moving through narrow spaces while partially deflated, then fully inflating components in order to lift debris or prevent further collapse of a structure. Planned activities in support of this project include the initiation of an annual one-day symposium bringing together robotics researchers from throughout the state of Utah. The project will explore co-design of mechanical structure and control algorithms for an inflatable, shape-changing robot capable of exerting large forces through distributed, soft actuation. These robots comprise a lattice of inflatable actuators whose individual volumetric changes are coordinated to produce useful motion and shape change. The output force of the robot, arising from the contribution of multiple actuators working together, will enable the robot to lift over 100x its own weight. Despite these high forces, the loads are distributed over a large surface area, allowing safe and comfortable interaction with delicate objects, such as people. A core contribution of this work is the development of a scalable kinematic model to enable precise actuator coordination and inform hardware design. Guided by modeling efforts, a variety of robot configurations look to be constructed and tested, and their performance compared for motion accuracy, payload capacity, and robustness to component failure or unexpected operational events. This research seeks to develop soft and strong robots that can adapt their shape to diverse tasks and environments to perform novel and important tasks. This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-08

Project Summary/Abstract The advent of cross coupling technologies has expanded access to the types of molecules available for drug screening and accelerated the synthesis of such compounds. Key to these advances in cross couplings is the discovery and application of privileged ligand scaffolds that enable high reactivity and broad substrate scope across different reaction types. In this proposal, we demonstrate that 2-phsophinoimidazole (2-PI) ligands represent a new privileged ligand class for addressing key challenges in cross coupling chemistry. Our preliminary data demonstrates that 2-PI ligands can access unique catalytic species by transforming into bidentate P–N coordination complexes, mixed N-H N-heterocyclic carbene/phosphinite complexes, or bimetallic complexes. Each of these types of catalysts achieves high reactivity in specific cross coupling reactions, and our efforts to access and favor specific catalyst structures in catalysis are providing productive solutions to difficult substrate classes in cross coupling reactions. In particular, the 2- PI ligands enable efficient catalysis in both Suzuki and Buchwald-Hartwig (BH) aminations with aryl chloride substrates, including for heterocyclic chlorides common in FDA approved pharmaceuticals. In aim 1, we will conduct structure activity studies to determine how ligand structure can influence and favor specific catalyst forms, whether it be the P–N coordination complex, N-H NHC/phosphinite formation, or bimetallic complex formation. We will then test and optimize these ligands and catalyst structures in Suzuki and BH amination reactions that employ challenging sterically hindered and heteroaryl chlorides, sensitive heteroaryl boronic acid nucleophiles, and sterically hindered amine nucleophiles. In aim 2, we will optimize catalyst structure with our 2-PI ligands to achieve efficient catalysis with a broad range of heteroaryl chloride substrates under mild reaction conditions. We will also expand our efforts to optimize Ullman-type couplings for C-O and C–S bond formation. In Aim 3, we will capitalize on the ability of our monosubstituted N-H NHC Pd complexes to perform H-bonding accelerated catalysis in Heck reactions with alkenyl alcohol substrates. We will also optimize reaction conditions for a tandem Heck/hydroalkoxylation reaction that generates tetrahydrofuran products in a single step. The studies presented herein are highly amenable to participation by undergraduate researchers and many of the optimization studies proposed will be led by senior undergraduates. The result of these studies will be new tools for synthetic and medicinal chemistry that capitalize on the potential of 2-PI ligands to transform into different metal complexes during catalysis.

NSF Awards · FY 2025 · 2025-08

This award will enable the development of advanced cyberinfrastructure to digitize and integrate over one million dragonfly and damselfly (Odonata) specimens from major natural history collections across the United States. The project, called Di-ODE (Digital Integration of Odonata), will create a unified, publicly accessible digital platform through Odonata Central, linking high-resolution specimen images with critical data such as collection localities and species identifications. This initiative will expand access to these important biological resources for scientists, educators, students, and the public. Di-ODE includes robust training programs to build skills in biodiversity data science and collections digitization. The project will enhance STEM education, promote data literacy, and engage community scientists, contributing to environmental awareness and scientific literacy. Through outreach and digital accessibility, Di-ODE will strengthen efforts to monitor environmental change and inform freshwater conservation across the globe. The project will transform how Odonata biodiversity data are accessed and analyzed by the research community. Dragonflies and damselflies are ecologically sensitive indicators of freshwater health and have been the focus of major studies in evolutionary biology, systematics, and biogeography. However, much of the valuable specimen data remains locked in poorly accessible physical collections. Di-ODE addresses this gap by creating efficient, scalable digitization workflows, using customized optical character recognition (OCR), advanced georeferencing, and data management tools. The resulting infrastructure will enable novel research in global change biology, comparative ecology, and phylogenetics. By improving data quality and access, Di-ODE will foster cross-disciplinary collaboration and provide a model for digitizing and mobilizing data from other invertebrate groups. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

An award is made to Brigham Young University (BYU) to enable modernized long-term preservation of over 530,000 herbarium specimens, including digital imaging and baseline recording of specimen data for 225,000 specimens not yet databased. Digitized specimen data produced through this project will be searchable and available via the internet to land managers and agency personnel, which will enhance sound management policies based on species distribution data. These data will also be searchable by the public, enabling opportunities for exploring nature and species diversity of the intermountain west by anyone. This project will involve training and leadership opportunities for one graduate student and many undergraduate students in a mentored-learning environment that emphasizes curatorial practices and various uses of specimen-based data. Student expertise will contribute to an expansive public Tree of Life exhibit being developed by the museum that highlights critical but often overlooked aspects and impacts of species diversity on everyday life. The Brigham Young University Herbarium is the largest herbarium in the intermountain west in size, species representation, and geographic breadth. This project will provide cabinetry and installation onto a compact storage system already installed by the university. The new storage system will preserve the value of existing specimens, by eliminating overcrowding that can lead to specimen damage, and will also provide space for future growth. The project will also support purchase of new cameras for higher-resolution imaging of specimens. Then, as specimens are moved into the new cabinetry, the images will be incorporated into a modernized organizational system that includes essential digital specimen information to improve the use of the collection by clientele. At the completion of this project, the entire BYU herbarium will be searchable online. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Forecasting complex societally important systems such as weather, ocean currents, and groundwater flow remains a grand challenge, especially when models must account for noisy or incomplete data. Traditional physics-based models offer scientific interpretability but often rely on idealized assumptions that limit accuracy. Conversely, recent advances in machine learning approaches have led to more accurate predictive models, but these are inherently "black boxes," lacking scientific insight into the underlying physical mechanisms. This project will use observable data to systematically adapt and modify existing physically derived models, thus staying true to both the traditional and data-driven approaches. The methodology is adaptable to a wide range of goals, such as optimizing predictions, matching observed statistics, or identifying unknown model parameters. Applications of this work will include problems of great interest to society and industry by identifying more efficient models for turbulence, which has ramifications from weather prediction to the development of engines and design of aircraft. The investigators will also apply this method to develop reliably predictive models for groundwater flow, a key issue for national water and food security. Further, the project will advance education and workforce development by mentoring undergraduate and graduate students and facilitating interactions with National Laboratories and private-sector stakeholders. The project builds on recent combined efforts of the investigators demonstrating an algorithm capable of "on-the-fly" parameter and model discovery. The investigators will mathematically rigorously justify this algorithm (and similar renditions of it), quantify its limitations, and lay the mathematical foundation for further improvement. The investigators will apply this method to identify large eddy simulation (LES) models for turbulent flows, and to correct reduced-dimensional models (e.g., from three dimensions to two dimensions) can be modified to accurately capture important statistics. The data assimilation technique will be extended to the Richards equation for groundwater flow, and the new parameter identification algorithm will be used to identify the precise form of spatially varying diffusion tensors which is critical for porous media and groundwater flows. The project will also develop algorithms for optimal sensor placement (where optimality is defined as accurately representing key characteristics of the system from partial observations available from limited mobile and/or static sensors), enhancing the ability to accurately reconstruct and predict full-system behavior from sparse measurements. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Algebraic geometry is the study of shapes, called varieties, that form the solutions of polynomial equations. These shapes arise naturally in physics, robotics, computer vision, statistics, and many other areas of science. Homotopy theory is the study of shapes up to deformation. Deformations provide a degree of flexibility to the study of varieties, which makes some difficult problems more tractable. A measurement that is not affected by deformations is called an invariant. Such measurements provide crucial information about varieties. This project will develop new invariants for varieties and offer novel reformulations of known, important invariants. A key feature of the invariants that will be developed is their validity in any number system, rather than just in the real or complex numbers. This will establish new connections between algebraic geometry, number theory, and low-dimensional topology. This project includes mathematical training opportunities for students across the country. There are three primary goals to the project. The first is to understand the motivic Euler characteristic, which is a quadratic form whose rank is the compactly supported Euler characteristic, of Hilbert schemes of K3 surfaces. This will involve a characterization of the Hasse--Witt invariants of the motivic Euler characteristic in terms of the bad reduction of the scheme. Other arithmetic aspects of these motivic Euler characteristics will also be studied. The second goal is explain the behavior of unstable local degrees in motivic homotopy theory under field extensions. In contrast to the stable case, the transfer map of the unstable local degree fails to be a homomorphism. The PI will construct an obstruction in Milnor--Witt K-theory to the homomorphicity of the transfer map. The payoff of the second goal is a toolkit for certain problems in enumerative geometry. The third goal of the project is to relate Levine's quadratic Donaldson--Thomas invariants, which are algebraically defined, to the Casson invariant in low-dimensional topology. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Wireless technologies are continually advancing, yet already deployed legacy devices remain in use for years, making it challenging to leverage the latest innovations. Upgrading wireless hardware is costly, particularly for long-term deployments. This project addresses this problem by enhancing the adaptability of existing wireless devices without needing hardware modifications. By utilizing overlooked aspects of wireless protocols, called protocol blind spots, this project plans to increase the capabilities of current hardware. To achieve this, the project will introduce subprotocols, which are software-based extensions that operate within an existing protocol. It is expected that wireless subprotocols will lead to enabling devices to enhance performance, improve resilience and adaptability, and communicate more effectively in dynamic environments. The innovation of this project lies in the design of wireless subprotocols, a novel approach to protocol creation by incorporating a fully functional subprotocol within a base protocol, complete with its own modulation, coding, and framing. Unlike traditional methods that depend on hardware upgrades, this approach leverages software to opportunistically exploit protocol blind spots, enabling enhancements such as extending the range of communication, improving spectrum coordination, and increasing adaptability. The project consists of three objectives: to establish a testbed for subprotocol development, to create innovative subprotocols, and to integrate these subprotocols into a software package that allows simultaneous operation of subprotocols on a device. By pushing the boundaries of software-defined wireless communications, this research is expected to influence future protocol designs and advance the field of wireless networking. The broader impact of this project extends to industry, academia, and the general public. Since subprotocols are software-based, they are expected to be easily deployable, facilitating industry adoption and fostering technology transfer. The project will also establish a new research area in wireless communications, influencing the design of future wireless devices and improving the adaptability of previously deployed devices. Additionally, this project's software-based nature lowers the barrier to entry, making wireless research more accessible to new students. Hands-on interactive activities and demonstrations will be developed to engage students and the community in wireless technology exploration. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-07

PROJECT SUMMARY/ABSTRACT Loss of functional beta cell mass is a hallmark of Type 1 and Type 2 diabetes. Increasing beta cell mass could be used as a treatment for diabetes. GLP1-R mediated signaling and inhibition of Dyrk1a activity are sufficient to increase functional beta cell mass. Nr4a1 is essential for beta cell proliferation and insulin secretion. Recent findings from our laboratory demonstrate that GLP1-R signaling results in upregulation of Nr4a1 in the beta cell. Similarly, Dyrk1a inhibition by Harmine, and Harmine derived compounds, results in upregulation of the Nfat family of transcription factors. We have shown that Nfatc2 and Nfac3 induce Nr4a1 expression, and that Nr4a1 deletion impairs Nfatc2 mediated beta cell proliferation. While these data suggest that Nr4a1 is essential for both the GLP-1R and Dyrk1a regulated beta cell proliferation pathways, and suggest an alternative target to expand functional beta cell mass, there are fundamental gaps in our understanding regarding Nr4a1 in these pathways, in terms of 1) the necessity of Nr4a1 in Exendin 4 and/or Harmine mediated beta cell proliferation, insulin secretion, and cell survival, 2) the effects of Exendin 4 and/or Harmine on Nr4a1 gene regulation in terms of binding partner interactions, genomic localization, and 3) how enhancing Nr4a1 expression and activity affects Exendin 4 and/or Harmine mediated beta cell proliferation. These gaps hinder the rationale design of targeted therapies to improve functional beta cell mass as a treatment for individuals with Type 1 and Type 2 diabetes. The long-term goal of our research is to develop strategies to improve beta cell function, proliferation and survival to improve patient outcomes. The overall objective of this proposal is to determine the role of Nr4a1 in the GLP1-R and Dyrk1a mediated pathways that expand functional beta cell mass. Our central hypothesis is that Nr4a1 is a key downstream that permits modulation of the GLP1-R and Dyrk1a pathways to enhance functional beta cell mass. Guided by our preliminary data, this hypothesis will be tested in the following specific aims: Aim 1: Determine the effect of Nr4a1 in GLP-1R and Dyrk1a mediated functional b-cell mass expansion. Aim 2: Determine the effect of Nr4a1 in the GLP-1R and Dyrk1a signaling pathway that leads to functional b-cell mass expansion. Aim 3: Determine the effect of Nr4a1 pharmacological modulation on GLP-1R and Dyrk1a mediated b- cell mass expansion. The proposal is innovative because it elucidates novel functions of Nr4a1 in these two proliferative pathways. The proposed research is significant because it fills fundamental gaps in our understanding of an understudied beta cell regulator, Nr4a1, its role in these critical pathways, and how its modulation can enhance functional beta cell mass.

NSF Awards · FY 2025 · 2025-06

This project leverages recent findings from neuroscience to create artificial intelligence (AI) language models that store knowledge and respond to input more like biological brains. This will be done by changing the flow of information through the language model in ways that make it more responsive to human emotions, more skilled at remembering and using information provide by humans, and better able to make fair and equitable decisions when the desires of many people come into conflict. This is important because many harms caused by AI systems can be traced to over-reliance on training data and an inability to adapt to the situation and needs of specific individuals. The new models will be rigorously tested in simulations where humans and AIs work together to make decisions and accomplish tasks, and will be probed to determine the potential benefits and/or risks introduced by this biologically-inspired computing paradigm. Additionally, this research will expand participation in science and technology by involving undergraduate students including a visiting research program that remotely hosts students from other universities. Research results will be shared via workshops, academic articles, and public media. This research will be conducted via three research thrusts, each addressing a specific biologically-inspired property that current language models lack, resulting in new open-source foundation models in the 7B-20B parameter range. Specifically, the research team will develop biologically inspired algorithms that emulate mirror neurons, long-term potentiation, and metaplasticity within transformer-based language models. The models created during this project will be evaluated in two ways: (a) via automated metrics that assess the emotional responsiveness and factual accuracy of the model, and (b) via direct human-large language model (LLM) interactions in multi-party scenarios where participants have conflicting priorities, and where the LLM has control over (low-risk) outcomes that affect humans. These studies are designed to preserve participant well-being while providing a valuable litmus test of language model behavior “in the wild”. It is anticipated that developed language models will be better able to manage contested resources, and more effective at responding appropriately to nuanced human emotions and experiences. They may also be uniquely suited to agentic scenarios that require the model to iteratively formulate objectives, plan actions, write and execute code, and deliver reasonable results back to humans in real-world scenarios with domain-specific constraints. This project is jointly funded by the Foundations of Emerging Technologies Program, the Robust Intelligence Program, and the Science of Learning and Augmented Intelligence Program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This award supports junior researcher travel to attend the 14th International Conference on Bayesian Nonparametrics that will be held on 23-27th, 2025 at UCLA's campus, Los Angeles, CA, USA. This conference is held biannually and is known to bring together leading experts and talented young researchers working on applications and theory of nonparametric Bayesian statistics. It is an official section meeting of the Bayesian Nonparametrics (BNP) section of the International Society for Bayesian Analysis (ISBA). The BNP 14 meeting is also co-sponsored by the Institute of Mathematical Statistics (IMS), and it is endorsed by the Section of Bayesian Statistical Sciences of the American Statistical Association. The main goals of the conference are to promote the interaction among scientists who develop and use BNP methods; to encourage discussions among researchers in various areas and fields of study; to foster cross-fertilization of ideas; to facilitate small group discussions among senior and junior researchers; and to disseminate results of the conference as widely as possible. More than 250 participants are expected to attend. BNP methods are statistical procedures that favor flexibility which permits relaxing many assumptions that are often times very rigid and/or hard to verify. Due to this, there has been a surge in the number of fields that have begun to employ BNP methods such as genetics, archaeology, psychology, economics, neuroscience among others. National Science Foundation support will enable the participation of 13 junior participants, including graduate students and postdoctoral scholars, at the conference. The International Conference on Bayesian Nonparametrics is the premier conference of BNP methods and allows weeklong discussions by including a good mix of methodological, applied, and theoretical sessions. The topics of these sessions include BNP connected to deep learning, Bayesian density estimation, scalable nonparametric regression, hazard rate and survival function estimation (without or with covariates), estimation of spectral distribution of a time series, estimation of conditional density and density regression, classification, clustering, and estimation of the distribution of latent variables such as random effects and so on. NSF participant support is directed to individuals with evidence of high-quality, early-career research accomplishments. All the verified invited and contributed speakers are listed on the conference website at https://bnp14.org/. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

This three-year REU Site: Robotic Pioneers and Extreme Environments offers a transformative 10-week research experience in advance robotics technologies for extreme environments, including GPS-denied areas, underwater settings, and space exploration. Each year, ten REU students will engage in research that addresses the critical challenges encountered by robots operating in harsh conditions. Participants will engage in research projects that focus on developing innovative solutions for autonomy and control, removing the need for humans to enter hazardous environments. Students will undertake individual research projects under the mentorship of expert faculty and graduate mentors, complemented by hands-on team activities such as designing and competing with robots. Beyond technical research, participants will build leadership and communication skills through STEM outreach initiatives and professional development sessions. Participants will not only expand their technical expertise but also gain the confidence and skills to make meaningful contributions to the rapidly evolving field of robotics. The program will expand participation in robotics by actively recruiting students from across the US. By cultivating the next generation of engineers and equipping them for pursuing graduate programs and potential careers in robotics and autonomous systems, the engineering workforce will be able to better address current and future societal challenges. One goal of this project is to develop accomplished engineers who are confident innovators in the robotics and autonomy communities. By providing in-depth education and research experiences to undergraduates, the project will provide opportunities for participants to deepen their knowledge and understanding about robotics systems and to consider pursuing graduate studies and potential careers in robotics related fields. Students will engage in four key activities: conducting individual research projects to gain a deep understanding of a specific robotics area, participating in a hands-on team project to build and compete with maze-running robots, teaching robotics to younger students at a STEM camp, and attending professional development activities, seminars and workshops, to prepare for future academic and professional pursuits. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-03

Abstract Emerging adulthood is a time when persons with type 1 diabetes (PWD) make major transitions into committed relationships. T1D partners (T1DP) play an important role in diabetes management during emerging adulthood in providing (or not) support. Although studies have demonstrated significant distress in PWD and T1DP, research has not examined the dyadic environment of diabetes management in relation to blood glucose. There is a critical need to identify how (a) dyadic health behaviors and (b) the dyadic communal coping of couples with T1D benefit diabetes management during emerging adulthood. Establishing patterns of health behaviors and dyadic T1D management routines that improve glycemic control during emerging adulthood are crucial as these patterns are likely to carry into adulthood. Our long-term goal is to develop dyadic behavioral interventions that facilitate healthy T1D management and decrease complications among emerging adults with T1D. Our overall objective is to identify how daily T1DP health behaviors (sleep health, physical activity, nutrition) and communal coping relate to glucose outcomes. We will test our central hypotheses that healthy partner behaviors and supportive communal coping will predict better glycemic control. We use daily diary and EMA methods to explore how partner health behaviors and communal coping relate to average glucose, time- in-range, and return to euglycemia following hypo/hyperglycemic excursions. The proposed study targets novel avenues of improving blood glucose outcomes in a critical period of life for those with T1D. To achieve this objective, we will (1) Identify how daily partner health behaviors relate to PWD glycemic control among 200 couples, across 14 days and (2) Identify partner communal coping behaviors during acute hypo/hyperglycemic events that facilitate timely return to euglycemia. The expected outcomes will demonstrate how partner communal coping during hypo/hyperglycemic events support a healthy return to euglycemia. It is urgent to examine the dyadic strategies of couples facing T1D at the critical developmental period of emerging adulthood when long-term partnerships and lifelong health habits are first formed. The proposed study is significant because it will provide the first dyadic evidence about this critically important period when PWD form lasting diabetes care habits in committed relationships. Ultimately, such knowledge has the potential to help set the stage for better PWD health outcomes in mid and later life.

NIH Research Projects · FY 2026 · 2025-03

PROJECT SUMMARY The cytosolic chaperonin CCT is a large protein complex that plays an indispensable role in maintaining the cellular proteome by assisting in the folding of numerous proteins with complex tertiary structures and unfavorable folding trajectories. Proper CCT function is vital to human vision as evidenced by the fact that inactivating mutations in CCT cause Leber Congenital Amaurosis (LCA). CCT contributes to the visual process by folding the cytoskeletal proteins actin and tubulin as well as other proteins with b-propeller folds that have essential functions in vison. These include the G protein b1 (Gb1) subunit of the visual G protein transducin, the G protein b5 (Gb5) subunit of the regulator of G protein signaling 9 (RGS9) dimer, and the BBS2 and BBS7 subunits of the Bardet-Biedl syndrome (BBS) ciliary transport complex, the BBSome. Despite the importance of CCT in maintaining the proteome, we know very little at the molecular level about how CCT assists in the folding of these b-propeller proteins and how mutations disrupt folding and cause disease. To address this gap in knowledge, we propose to determine the structures of human Gb1 and Gb5 and their disease-causing mutants. Structures of Gb5 bound to CCT and its co-chaperone PhLP1 show progressive step-by-step formation of the Gb5 b-propeller that reveals its folding trajectory. Unraveling how CCT influences the folding trajectory of a b-propeller protein represents a breakthrough in understanding chaperone-mediated protein folding. Moreover, applying these same techniques to misfolding and disease-causing mutants of Gb1 and Gb5 will show how the mutations disrupt their folding trajectories. Finally, we propose to employ our biochemical and high resolution cryo-EM expertise to understanding biogenesis of the BBSome complex. A key step in BBSome assembly is the formation of the BBS2-BBS7 dimer, which requires both CCT and three chaperonin- like (CL-BBS) proteins BBS6, BBS10 and BBS12 to come together. Despite the 18 years since CL-BBS protein discovery and the predominant role their mutations play in causing BBS, the molecular mechanism by which the CL-BBS proteins and CCT assist in BBS2 and BBS7 folding and BBS2-BBS7 dimer formation is unknown. The proposed studies will fill this gap in knowledge and will deepen understanding of the molecular defects caused by mutations in Gb subunits, BBS7 and CL-BBS proteins. The structural work will establish a foundation for targeted, structure-based drug design to create new therapies for the retinopathies, neuropathies and ciliopathies caused by these mutations.

NIH Research Projects · FY 2025 · 2025-02

Program Director/Principal Investigator (Last, First, Middle): Weber, K. Scott Project Summary Graves’ Disease (GD) is the fourth most common autoimmune disease in the United States, affecting ~6.5 million people, that primarily results in hyperthyroidism. The immunopathogenesis of the disease is initiated by autoreactive B cells which secrete antibodies that bind to the thyroid stimulating hormone receptor (TSHR). These anti-TSHR autoantibodies (TRAbs) are the critical cause of disease. They bind to TSHR on thyroid cells, causing chronic stimulation and overproduction of thyroid hormones. Currently, there are no treatments available to address the disease causing mechanism of GD, the TRAb producing B cells. The goal of this research is to apply the concept of chimeric antigen receptor (CAR) T cell therapy to the development of a novel and potentially curative treatment for GD. This will be done by generating a chimeric autoantigen receptor (CAAR), which replaces the binding domain of a standard CAR with a fragment of the TSHR. The TSHR fragment acts as bait for autoreactive B cells, because the B cell receptors (BCR) on their surface bind to TSHR just as soluble TRAbs do. We have engineered TSHR CAAR T cells, and our preliminary data show that the CAAR T cells bind to anti-TSHR Abs and B cell receptors, activate significantly, and can specifically eliminate anti-TSHR B cells, but not other B cells. We will further characterize these GD CAAR T cells by performing the following aims. 1) We will perform several cytotoxicity assays to determine the efficacy of our CAAR T cells at eliminating anti-TSHR B cells while not harming other B cells. Flow cytometry based cytotoxicity, proliferation, cytokine secretion assays, and a direct cytotoxicity assay will be performed. 2) We will evaluate the influence of soluble TRAbs in GD patients that will likely bind to our CAAR T cells, which could have an inhibitory or activating effect. We will perform cytotoxicity experiments in the presence of physiologically relevant levels of commercially available TRAbs and GD patient serum. We will also evaluate cytotoxicity in the presence of thyroid stimulating hormone to determine its possible effect on the CAAR T cells. 3) We will develop a bispecific LINK CAAR to further increase the safety and specificity of the treatment, by requiring binding to anti-TSHR BCR/Ab and a B cell marker, CD19, to initiate the CAAR T cell cytotoxicity. These LINK CAAR T cells will be compared to our original CAAR T cells for their efficiency, specificity, and ability to activate only on target. Thus, this project applies concepts of CAR T cell therapy to the development of a novel and potentially curative immunotherapy treatment for Graves’ disease. Project Summary

NSF Awards · FY 2025 · 2025-01

This Major Research Instrumentation (MRI) award is for a multipurpose X-ray diffractometer that meets the growing need for X-ray scattering and diffraction techniques at Brigham Young University (BYU) and the surrounding universities. It provides several capabilities that are novel and unique in the region. Using the new instrument, researchers across chemistry, physics, geology, and engineering can study the atomic structure of advanced materials at new depths, leading to more lightweight vehicles, improved magnetic memory systems, new imaging methods, and more efficient synthesis of medical and industrial chemicals. The system is also a central component of student experiential learning at BYU, where half of student researchers using the facilities are undergraduates. Because BYU consistently ranks in the top universities whose undergraduates go on to receive doctoral degrees, the instrument is significantly impacting the next generation of scientists. The instrument is also a vital research tool for half of the women faculty in chemistry and physics at BYU and is used in outreach efforts such as summer camps designed to increase the participation of female and Hispanic students in science in Utah. The instrument features high resolution powder diffraction, grazing incidence diffraction, X-ray reflectivity, texture analysis, microdiffraction, and capillary diffraction. Nearly all are new capabilities for academic institutions in Utah. These features are essential in a wide range of research projects aimed at understanding and optimizing current materials, designing new materials, and improving synthetic methods. A few specific examples include: (a) studying the microstructure in strained metals to facilitate lightweighting, (b) understanding the relationships between magnetic, electronic, and structural properties of emerging quantum materials (c) designing, optimizing, and orienting new materials that can generate terahertz frequencies of light for use in imaging, chemical monitoring, and communication, and (d) creating new methods of synthesizing bimetallic thin films for use in catalysis. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-12

Modern manufacturing techniques reduce price and increase quality of many of the products we use every day. However, buildings continue to be manufactured on-site with high levels of skilled manual labor. This leads to higher costs and lower quality, which are a key factor in the affordable housing crisis across most of the country. Additionally, the use of manual methods requires standardization in many details rather than optimizing for reduced material usage. Automation in building construction methods has been limited by the difficulty of transporting large components and the desire to customize buildings. This Boosting Research Ideas for Transformative and Equitable Advances in Engineering (BRITE) Pivot award seeks to develop a new manufacturing approach that will enable fabrication of customized building systems in a compact state. These compact components and forms can be readily shipped and then deployed on-site. This innovation is expected to decrease the costs of housing to increase affordability. The project will also develop a K-12 curriculum to increase awareness of STEM career opportunities among first- and second-generation Hispanic children. The project will apply the little-used sheet-lamination additive manufacturing (3D Printing) process to fabricate origami. Sheet lamination applications have been limited when used to create monolithic structures, but sheet lamination offers strong advantages for creating origami since it leverages the strengths of the native sheets. The digital control of additive manufacturing allows for easy customization. By selectively bonding and cutting stacked sheets, origami-inspired deployable systems will be fabricated. This approach will enable low-cost, high-volume production of building components such as concrete forms in a compact shape for easy transport. To achieve these objectives, kinematic models and fabrication methods will be developed and methods of converting traditional single sheet origami designs for manufacturing as stacks of sheets will be created. The manufacturing process will be extended to fiber-reinforced composites by developing methods of creating local hinges in vacuum-infused sheets. Strategies for improving interlaminar peel strength will also be evaluated. Kinematic solutions for fabricating open structures and containers these stacked sheet configurations will be developed and demonstrated as scaled models of building structures and/or formwork. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.